Pervasive Intelligent Traffic Surveillance System obtains traffic flow-related information through deep learning networks. Through this system, it can analyze and obtain real-time traffic flow information in lane levels from the roadside camera, and can detect specific abnormal events of the road section, such as road congestion, changing lanes in violation, etc. The system uses Scalable Amplified Group Environment 2 (SAGE2) to present the entire supervisory interface, so multiple real-time traffic images and analysis data can be presented on a multi-screen TV wall.This system is to realize the real-time traffic flow monitoring system by combining the image recognition network of YOLOv3 and the multiple objects tracking algorithm. Through this system, it is possible to automatically monitor the streaming image information of multiple roadside cameras, and to provide more relevant traffic flow information to the units that will pass through the application of abnormal event detection and violation detection.The intelligent traffic monitoring system developed by this technology can be combined with control ability to become a traffic management system in the future, and further plan or control the traffic flow of each road segment; long-term collection of domestic road traffic dataset can strengthen the detection speed and accuracy of deep learning, and improve the current network; in the future, it will be possible to combine traffic flow information and traffic conditions into vehicle navigation systems or automated driving systems to provide the best route planning.